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Bachrach, Ran1, Nader Dutta1
(1) Schlumberger RS/DCS, Houston, TX

ABSTRACT: Seismic Reservoir Description and Uncertainty Estimation: Examples from Clastic Basins

Seismic reservoir description (SRD) effort is based on mapping seismic attributes such as P impedance, shear impedance, and density into reservoir properties such as lithology, saturation, and porosity. The problem is non-unique, and thus, uncertainty is associated with the predictions. In many cases, geological knowledge can be used to reduce the inherent non-uniqueness of the SRD problem. However, introducing such information in quantitative manner can often be difficult.
In this presentation, we show two ways to incorporate geological interpretation and modeling into seismic reservoir description. We use Bayesian lithology classification techniques to derive lithology classes from rock physics, seismic inversion, and well-log data analysis. We then show how structural interpretation derived from wave kinematics can be combined with the seismic inversion derived from wave dynamics to generate prior models that will be used within a Bayesian classification framework to enhance the resolution of seismic reservoir description efforts. This approach can use geological modeling or detailed seismic interpretation to derive the most likely lithofacies given seismic response. We then show how we can extend this analysis in depth by accounting for theoretical or empirical compaction curves to generate a depth-dependent classification function. We show how accounting for this type of secondary information can reduce uncertainty in seismic property description. We provide examples from two clastic basins (deep water) where this methodology has been tested.


AAPG Search and Discovery Article #90026©2004 AAPG Annual Meeting, Dallas, Texas, April 18-21, 2004